![[2509.19975] From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2509.19975] From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Summary
This article introduces a new paradigm for probabilistic forecasting, proposing 'Probabilistic Scenarios' as an alternative to traditional sampling methods, enhancing accuracy and efficiency in predictions.
Why It Matters
The shift from sampling to a scenario-based approach in probabilistic forecasting addresses key limitations such as computational costs and lack of explicit probabilities. This innovation could significantly improve forecasting models across various applications, making it highly relevant for researchers and practitioners in machine learning and data science.
Key Takeaways
- Probabilistic Scenarios offer a new framework for forecasting, avoiding traditional sampling pitfalls.
- The proposed TimePrism model achieves state-of-the-art results on multiple benchmark datasets.
- This approach focuses on producing finite sets of scenario-probability pairs, enhancing interpretability.
- The paradigm shift could lead to more efficient and accurate forecasting models.
- The research opens new avenues for exploration in probabilistic modeling.
Computer Science > Machine Learning arXiv:2509.19975 (cs) [Submitted on 24 Sep 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting Authors:Xilin Dai, Zhijian Xu, Wanxu Cai, Qiang Xu View a PDF of the paper titled From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting, by Xilin Dai and 3 other authors View PDF HTML (experimental) Abstract:Most state-of-the-art probabilistic time series forecasting models rely on sampling to represent future uncertainty. However, this paradigm suffers from inherent limitations, such as lacking explicit probabilities, inadequate coverage, and high computational costs. In this work, we introduce \textbf{Probabilistic Scenarios}, an alternative paradigm designed to address the limitations of sampling. It operates by directly producing a finite set of \{Scenario, Probability\} pairs, thus avoiding Monte Carlo-like approximation. To validate this paradigm, we propose \textbf{TimePrism}, a simple model composed of only three parallel linear layers. Surprisingly, TimePrism achieves 9 out of 10 state-of-the-art results across five benchmark datasets on two metrics. The effectiveness of our paradigm comes from a fundamental reframing of the learning objective. Instead of modeling an entire continuous probability space, the model learns to represent a set of plausible scenarios and corresponding probabilities. Our work demonstrates the potential of the P...
